Health Care‐Related Correlates of Cervical Cancer Screening among Sexual Minority Women: An Integrative Review
Bibliographic record
Abstract
INTRODUCTION: Sexual minority women (SMW; lesbian, bisexual, nonheterosexual women) may have lower rates of cervical cancer screening than heterosexual women. Health care-related factors may explain some of the variation in cervical cancer screening rates among SMW. We aimed to synthesize published evidence of health care-related correlates of cervical cancer screening among SMW. METHODS: We searched PubMed, CINAHL, and PsycINFO databases for English-language studies published between January 2000 and March 2017 that 1) assessed sexual identity or the sexual partners of female participants, 2) included cervical cancer screening as a main outcome of interest, and 3) measured at least one health care-related variable in addition to cervical cancer screening. We excluded articles that 1) reported on non-US samples or 2) did not report original research. We reviewed the sample, methods, and findings of 17 studies. We then summarized current knowledge about health care-related factors across 3 categories and generated recommendations for clinical practice and future research. RESULTS: Several health care-related factors such as previous contraception use, having a primary care provider, knowledge of screening recommendations, and disclosing sexual orientation to providers were consistently positively associated with cervical cancer screening. Three groups of factors-previous health care use, health care provider-related factors, and belief-related factors-account for a substantial part of the variation in cervical cancer screening among SMW. DISCUSSION: Several gaps in knowledge remain that could be addressed by recruiting more diverse samples of SMW with improved generalizability. Clinicians and clinical institutions can address factors associated with low rates of screening among SMW by preventing sexual orientation-based discrimination, inviting sexual orientation disclosure, and offering cervical cancer screening to SMW at a variety of health care encounters. Future research should examine how the location of care and health care provider type affect SMW's cervical cancer screening behaviors and should test the effectiveness of health care interventions designed to address sexual orientation-related disparities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".